ALBERT

Description: L-band radiance measurements such as these from the SMOS satellite can be used to distinguish thin from thick ice under cold surface conditions. However, uncertainties can be large due to assumptions in the forward model that converts brightness temperatures into ice thickness, and due to uncertainties in ancillary fields which need to be independently modelled or observed. It is therefore advisable to perform a critical assessment with independent observational and model data, before using these data for model validation or data assimilation. Here, we discuss version 3.1 of the University of Hamburg L3C SMOS sea-ice thickness data set (SMOS-SIT) from autumn 2010 to spring 2017, and compare it to the results of the global ocean-sea ice analysis ORAS5. It is concluded that SMOS-SIT provides valuable and unique information on thin sea ice during winter, both in terms of the seasonal evolution and interannual variability. Overall, there is a promising match between SMOS-SIT and ORAS5 early in the freezing season (October-December), while later in winter, sea ice is consistently modelled thicker than observed. This seems to be mostly due to deficiencies of the model to simulate polynyas and fracture zones. However, there are regions where biases in the observational data seem to play a role, as comparison to independent observational data suggests. Both the reanalysis and the observations are provided with uncertainty estimates. While the reanalysis uncertainty estimates for the thickness of thin sea ice are probably too small and do not include structural uncertainty of the simulation, these of SMOS-SIT are often large, and do not seem to adequately characterise the complex uncertainties of the retrieval model. Therefore, careful and manual assessment of the data when using it for model evaluation and data assimilation is advisable. Interannual variability and trends of the large-scale distribution of thin sea ice are in good agreement between SMOS-SIT and ORAS5. In summary, SMOS-SIT presents a unique source of information about thin sea ice in the winter-time Arctic, and its use in sea ice modelling, assimilation and forecasting application is nascent and promising.

Description: The ECMWF OCEAN5 system is a global ocean and sea-ice ensemble of reanalysis and real-time analysis. This manuscript gives a full description of the OCEAN5 system, with the focus on upgrades of system components with respect to its predecessors ORAS4 and ORAP5. An important novelty in OCEAN5 is the ensemble generation strategy that includes perturbation of initial conditions, and a generic perturbation scheme for observations and forcing fields. Other upgrades include revisions to the a-priori bias correction scheme, observation quality control and assimilation method for sea-level anomaly. The OCEAN5 historical reconstruction of the ocean and sea-ice state is the ORAS5 reanalysis, which includes 5 ensemble members and covers the period from 1979 onwards, and with a backward extension until 1958. Updated version of observation data sets are used in ORAS5 production, with special attention devoted to the consistency of sea surface temperature (SST) and sea-ice observations. Assessment of ORAS5 in the observation space suggests that assimilation of observations contribute to reducing the analysis error, with the most prominent contribution from direct assimilation of ocean in-situ observations. Results of observing system experiment further suggest that Argo float is the most influential observation type in our data assimilation system. Assessment of ORAS5 has also been carried out with several key ocean state variables and verified against independent observation data sets from ESA CCI project. With respect to ORAS4, ORAS5 has improved ocean climate state and variability in terms of SST and sea-level, mostly due to increased model resolution and updates in assimilated observation data sets. In spite of the improvements, ORAS5 still underestimates the temporal variance of sea level, and continue exhibiting large SST biases in the Gulf Stream and extension regions which is possibly associated with misrepresentation of front positions. Overall, the SST and sea-ice uncertainties estimated using five ORAS5 ensemble members have spatial patterns consistent with those of analysis error. The ensemble spread of sea-ice is commensurable with the sea-ice analysis error. On the contrary, the ensemble is under-dispersive for SST.

Description: The ECMWF OCEAN5 system is a global ocean and sea-ice ensemble of reanalysis and real-time analysis. This paper gives a full description of the OCEAN5 system, with the focus on upgrades of system components with respect to its predecessors, ORAS4 and ORAP5. An important novelty in OCEAN5 is the ensemble generation strategy that includes perturbation of initial conditions and a generic perturbation scheme for observations and forcing fields. Other upgrades include revisions to the a priori bias correction scheme, observation quality control and assimilation method for sea-level anomalies. The OCEAN5 historical reconstruction of the ocean and sea-ice state is the ORAS5 reanalysis, which includes five ensemble members and covers the period from 1979 onwards. Updated versions of observation data sets are used in ORAS5 production, with special attention devoted to the consistency of sea surface temperature (SST) and sea-ice observations. Assessment of ORAS5 through sensitivity experiments suggests that all system components contribute to an improved fit to observation in reanalyses, with the most prominent contribution from direct assimilation of ocean in situ observations. Results of observing system experiments further suggest that the Argo float is the most influential observation type in our data assimilation system. Assessment of ORAS5 has also been carried out for several key ocean state variables and verified against reference climate data sets from the ESA CCI (European Space Agency Climate Change Initiative) project. With respect to ORAS4, ORAS5 has improved ocean climate state and variability in terms of SST and sea level, mostly due to increased model resolution and updates in assimilated observation data sets. In spite of the improvements, ORAS5 still underestimates the temporal variance of sea level and continues exhibiting large SST biases in the Gulf Stream and its extension regions which are possibly associated with misrepresentation of front positions. Overall, the SST and sea-ice uncertainties estimated using five ORAS5 ensemble members have spatial patterns consistent with those of analysis error. The ensemble spread of sea ice is commensurable with the sea-ice analysis error. On the contrary, the ensemble spread is under-dispersive for SST.

Description: L-band radiance measurements of the Earth's surface such as those from the SMOS satellite can be used to retrieve the thickness of thin sea ice in the range 0–1 m under cold surface conditions. However, retrieval uncertainties can be large due to assumptions in the forward model, which converts brightness temperatures into ice thickness and due to uncertainties in auxiliary fields which need to be independently modelled or observed. It is therefore advisable to perform a critical assessment with independent observational and model data before using sea-ice thickness products from L-band radiometry for model validation or data assimilation. Here, we discuss version 3.1 of the University of Hamburg SMOS sea-ice thickness data set (SMOS-SIT) from autumn 2011 to autumn 2017 and compare it to the global ocean reanalysis ORAS5, which does not assimilate the SMOS-SIT data. ORAS5 currently provides the ocean and sea-ice initial conditions for all coupled weather, monthly and seasonal forecasts issued by ECMWF. It is concluded that SMOS-SIT provides valuable and unique information on thin sea ice during winter and can under certain conditions be used to expose deficiencies in the reanalysis. Overall, there is a promising match between sea-ice thicknesses from ORAS5 and SMOS-SIT early in the freezing season (October–December), while later in winter, sea ice is consistently modelled thicker than observed. This is mostly attributable to refrozen polynyas and fracture zones, which are poorly represented in ORAS5 but easily detected by SMOS-SIT. However, there are other regions like Baffin Bay, where biases in the observational data seem to be substantial, as comparisons with independent observational data suggest. Despite considerable uncertainties and discrepancies between thin sea ice in SMOS-SIT and ORAS5 on local scales, interannual variability and trends of its large-scale distribution are in good agreement. This gives some confidence in our current ability to monitor climate variability and change in thin sea ice. With further improvements in retrieval methods, forecast models and data assimilation methods, the huge potential of L-band radiometry to derive the thickness of thin sea ice in winter will be realised and will provide an important building block for improved predictions in polar regions.

Description: In this paper we describe SEAS5, ECMWF’s fifth generation seasonal forecast system, which became operational in November 2017. Compared to its predecessor, System 4, SEAS5 is a substantially changed forecast system. It includes upgraded versions of the atmosphere and ocean models at higher resolutions, and adds a prognostic sea ice model. Here, we describe the configuration of SEAS5 and summarise the most noticeable results from a set of diagnostics including biases, variability, teleconnections and forecast skill. An important improvement in SEAS5 is the reduction of the Equatorial Pacific cold tongue bias, which is accompanied by a more realistic ENSO amplitude and an improvement in ENSO prediction skill over the central-west Pacific. Improvements in two-metre temperature skill are also clear over the tropical Pacific. SST biases in the northern extratropics change due to increased ocean resolution, especially in regions associated with western boundary currents. The increased ocean resolution exposes a new problem in the northwest Atlantic, where SEAS5 fails to capture decadal variability of the North Atlantic subpolar gyre, resulting in a degradation of DJF two-metre temperature prediction skill in this region. The prognostic sea ice model improves seasonal predictions of sea ice cover, although some regions and seasons suffer from biases introduced by employing a fully dynamical model rather than the simple, empirical scheme used in System 4. There are also improvements in two-metre temperature skill in the vicinity of the Arctic sea-ice edge. Cold temperature biases in the troposphere improve, but increase at the tropopause. Biases in the extratropical jets are larger than in System 4: extratropical jets are too strong, and displaced northwards in summer. In summary, development and added complexity since System 4 has ensured SEAS5 is a state-of-the-art seasonal forecast system which continues to display a particular strength in ENSO prediction.

Description: In this paper we describe SEAS5, ECMWF's fifth generation seasonal forecast system, which became operational in November 2017. Compared to its predecessor, System 4, SEAS5 is a substantially changed forecast system. It includes upgraded versions of the atmosphere and ocean models at higher resolutions, and adds a prognostic sea-ice model. Here, we describe the configuration of SEAS5 and summarise the most noticeable results from a set of diagnostics including biases, variability, teleconnections and forecast skill. An important improvement in SEAS5 is the reduction of the equatorial Pacific cold tongue bias, which is accompanied by a more realistic El Niño amplitude and an improvement in El Niño prediction skill over the central-west Pacific. Improvements in 2 m temperature skill are also clear over the tropical Pacific. Sea-surface temperature (SST) biases in the northern extratropics change due to increased ocean resolution, especially in regions associated with western boundary currents. The increased ocean resolution exposes a new problem in the northwest Atlantic, where SEAS5 fails to capture decadal variability of the North Atlantic subpolar gyre, resulting in a degradation of DJF 2 m temperature prediction skill in this region. The prognostic sea-ice model improves seasonal predictions of sea-ice cover, although some regions and seasons suffer from biases introduced by employing a fully dynamical model rather than the simple, empirical scheme used in System 4. There are also improvements in 2 m temperature skill in the vicinity of the Arctic sea-ice edge. Cold temperature biases in the troposphere improve, but increase at the tropopause. Biases in the extratropical jets are larger than in System 4: extratropical jets are too strong, and displaced northwards in JJA. In summary, development and added complexity since System 4 has ensured that SEAS5 is a state-of-the-art seasonal forecast system which continues to display a particular strength in the El Niño Southern Oscillation (ENSO) prediction.